This article is part of a special issue on epigenomics.

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Aging effects on DNA methylation modules in human brain and blood tissue

Steve Horvath12*, Yafeng Zhang2, Peter Langfelder1, René S Kahn3, Marco PM Boks3, Kristel van Eijk34, Leonard H van den Berg5 and Roel A Ophoff26

Author Affiliations

1 Department of Human Genetics, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA

2 Department of Biostatistics, School of Public Health, University of California Los Angeles, Los Angeles, CA 90095, USA

3 Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands

4 Department of Medical Genetics, University Medical Center Utrecht, Utrecht, The Netherlands

5 Department of Neurology, University Medical Center Utrecht, Utrecht, The Netherlands

6 UCLA Center for Neurobehavioral Genetics, Semel Institute of Neuroscience and Human Behavioral, School of Medicine, University of California Los Angeles, Los Angeles, CA 90095, USA

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Genome Biology 2012, 13:R97  doi:10.1186/gb-2012-13-10-r97

Published: 3 October 2012

Additional files

Additional file 1:

Schizophrenia status has a negligible effect on aging effects. A scatterplot of correlation test P-values for correlations between age and methylation profiles in schizophrenia cases (x-axis) and healthy controls (y-axis) based on the Dutch whole blood data sets (data sets 2 and 3). Additional file 1 shows that schizophrenia disease status has a negligible effect on aging-related changes for the vast majority of CpG sites.

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Additional file 2:

Age effects in different brain regions. Scatterplots of correlation test P-values for correlations between age and methylation profiles in the four brain regions (data sets 7 to 10). Overall, these P-values are highly correlated, which shows that age has a similar effect in all four brain regions.

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Additional file 3:

Module preservation analysis. The figures report the results of the module preservation analysis in the validation data sets. Each figure (page) corresponds to one validation data set. The left and right panels of each figure show the results for the Zdensity and medianRank statistics, respectively. The higher the value of the Zdensity statistic (and the lower the value of the median rank statistic), the stronger the evidence that the consensus module (based on the ten reference data sets) is preserved in the validation data set. The Zdensity statistic is based on a permutation test that allows one to establish significance thresholds (that are indicated by the horizontal lines at values 2 and 5 in the left panel). Values of Zdensity larger than 5 indicate moderate preservation while values below 2 indicate no evidence of preservation.

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Additional file 4:

1,000 CpGs with highest average module membership in the green aging module. The comma delimited file reports the Illumina array probe identifiers of the 1,000 CpG sites with highest average module membership (kME) with respect to the aging-related (green) module. This table also reports the average kME value and the gene symbols of neighboring genes. Further, it contains additional probe annotations. Column SNPpolymorphicCpGfromChen2011 indicates which of the CpGs is known to contain a common SNP [38]. Column NumberOfMatchingBasesToCrossReactiveTarget indicates which CpGs are non-specific (NA means it is specific) according to [38].

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Additional file 5:

Gene ontology enrichment chart of the 1,000 aging module. The Excel table shows the results of a gene ontology enrichment analysis using the DAVID software when 'GO Chart' output is selected.

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Additional file 6:

Gene ontology enrichment cluster of the 1,000 aging module. The Excel table shows the results of a gene ontology enrichment analysis using the DAVID software when 'GO Cluster' output is selected.

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Additional file 7:

Enrichment analysis using the userListEnrichment function. The comma delimited file shows the results of a gene list enrichment analysis using the userListEnrichment function [25]. This function was used to assess whether the top 1,000 aging-related module genes (highest average kMEgreen) are significantly enriched (hypergeometric test) with genes that are part of the brain-, blood- and stem cell-related lists curated from the literature. The userListEnrichment function was used to study the properties of lists of genes whose promoters contain CpG sites that are part of the aging related (green) module.

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Additional file 8:

Analysis overview. The figure shows the analysis steps of the consensus network analysis and their rationale.

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